Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.
| Published in | American Journal of Neural Networks and Applications (Volume 5, Issue 1) |
| DOI | 10.11648/j.ajnna.20190501.12 |
| Page(s) | 7-11 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Artificial Intelligence, Neural Network, Sigmoid Function, Neurons, Nodes
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APA Style
Mohaiminul Islam, Guorong Chen, Shangzhu Jin. (2019). An Overview of Neural Network. American Journal of Neural Networks and Applications, 5(1), 7-11. https://doi.org/10.11648/j.ajnna.20190501.12
ACS Style
Mohaiminul Islam; Guorong Chen; Shangzhu Jin. An Overview of Neural Network. Am. J. Neural Netw. Appl. 2019, 5(1), 7-11. doi: 10.11648/j.ajnna.20190501.12
AMA Style
Mohaiminul Islam, Guorong Chen, Shangzhu Jin. An Overview of Neural Network. Am J Neural Netw Appl. 2019;5(1):7-11. doi: 10.11648/j.ajnna.20190501.12
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Download@article{10.11648/j.ajnna.20190501.12,
author = {Mohaiminul Islam and Guorong Chen and Shangzhu Jin},
title = {An Overview of Neural Network},
journal = {American Journal of Neural Networks and Applications},
volume = {5},
number = {1},
pages = {7-11},
doi = {10.11648/j.ajnna.20190501.12},
url = {https://doi.org/10.11648/j.ajnna.20190501.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.12},
abstract = {Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.},
year = {2019}
}
TY - JOUR T1 - An Overview of Neural Network AU - Mohaiminul Islam AU - Guorong Chen AU - Shangzhu Jin Y1 - 2019/06/29 PY - 2019 N1 - https://doi.org/10.11648/j.ajnna.20190501.12 DO - 10.11648/j.ajnna.20190501.12 T2 - American Journal of Neural Networks and Applications JF - American Journal of Neural Networks and Applications JO - American Journal of Neural Networks and Applications SP - 7 EP - 11 PB - Science Publishing Group SN - 2469-7419 UR - https://doi.org/10.11648/j.ajnna.20190501.12 AB - Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution. VL - 5 IS - 1 ER -
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